Neuroscience
Can Brain Activity Predict Criminal Reoffending? The previous post discussed a functional MRI study suggesting that the level of error-related activation in the anterior cingulate cortex (ACC) might have value in predicting whether a recently released prisoner will be rearrested within 4 years (Aharoni et al. 2013):
However, using ACC activity as a dichotomous variable misclassified 40% of low ACC participants who did not reoffend and 46% of high ACC participants who did commit crimes after release, not exactly the odds you'd want for making parole decisions. Even the senior author was doubtful that an fMRI test would ever be useful for risk assessment purposes on a case by case basis.
Since Aharoni and colleagues made their individual subject data available as supplementary material (Download Dataset_S01, XLSX), I was interested in how some of the demographic and performance variables might be related to recidivism, since these are obviously cheaper and easier to collect from incarcerated prisoners than MRI scans.
The cognitive task performed during the fMRI experiment required responding to a frequent stimulus presented 84% of the time ("X") and inhibiting responses to a rare stimulus ("K").
The study compared brain activity on incorrect responses to "K" (commission errors) and correct responses to "X" (hits) in a region of interest in the dorsal ACC, which has been implicated in error processing (Simons, 2010), among many other things. The authors framed the results largely in the context of impulse control, but other explanations are possible (as we'll see later).
Are any of the task performance variables related to recidivism? Starting with some very simple-minded t-tests, the rate of commission errors in the group of participants arrested for nonviolent offenses1 (n=40) did not differ significantly from what was seen in those not arrested again (n=56).2
Data from (Aharoni et al., 2013). Commission errors in the Go/NoGo task (% incorrect responses on NoGo trials) and omission errors (% missed responses on Go trials) for inmates that went on to commit nonviolent offenses within 4 years after release (Nonviolent) and those that did not (None). The trend for the reoffenders to commit more errors was not significant (p=.09) even without correcting for multiple comparisons.
Although there were data from a large control group of nonoffenders (n=102) used to set the ACC ROI, we don't have their behavioral results. I consulted an earlier fMRI paper by Kiehl et al. (2000) that used a very similar Go/NoGo task in 14 control participants. Commission errors occurred on 23.7% of NoGo Trials and omission errors on 3% of Go Trials, which is similar to what was seen in the offenders (overall means of 25.04% and 3.44%, respectively).
Reaction times (RTs) did not differ between the two offender groups either, suggesting there wasn't a differential speed-accuracy tradeoff (e.g., if the reoffenders were slower yet making marginally more errors).
Data from (Aharoni et al., 2013). RTs in milliseconds for commission errors (incorrect responses on NoGo trials) and hits (correct responses on Go trials) for inmates that went on to commit nonviolent offenses within 4 years after release (Nonviolent) and those that did not (None). There were no group differences.
Surprisingly, RTs were slower on commission errors (358 ms) than on hits (346 ms), a small but highly significant difference (p=.0005). This is the opposite of what you'd expect if the errors were due to impulsive responses. If the participants were becoming careless and not fully evaluating the NoGo stimulus, they'd be faster on error trials. This is why I'm not convinced the ACC activations are entirely related to behavioral impulsivity. In EEG studies of error processing, the degree of ACC activity3 is related to the emphasis placed on accuracy (Gehring et al., 1993), so if the reoffenders didn't care as much about accuracy, this could account for their low ACC status. One interesting bit of data for the authors to examine would be RT and accuracy on responses following an error, which indicates the amount of behavioral adjustment after making a mistake. Did the reoffenders show a lower propensity to slow down and become more careful? If so, this might reflect a lack of concern about the consequences of their actions.
However, the most puzzling thing to me were scores on Factor 2 of the Psychopathy Checklist-Revised (PCL-R) (Hare, 2003). Factor 2 is thought to reflect impulsivity, stimulation seeking, and irresponsibility (Ermer et al., 2012). The rearrested and not-rearrested groups were significantly different as expected, but in the opposite direction (unless I'm missing something here) — scores were lower in the group that was rearrested, in comparison to those who were not (p=.001).
Data from (Aharoni et al., 2013). Beta-weights from the dACC region of interest and a control region in medial prefrontal cortex (mPFC). PCL-R f2 is score on Factor 2 of the Psychopathy Checklist-Revised, normalized using a log transform (p=.001). [ROIs and PCL-R not measured using the same units, obviously.]
In the paper, Aharoni and colleagues noted that age at release and Factor 2 scores showed predictive effects along with ACC activity. This was only when nonviolent crimes were considered [remember that only nine participants were arrested again for violent crimes]. Some research suggests that the PCL-R may predict violent recidivism, but other work questions this assertion.4 I'm definitely not the expert here, so please weigh in if you have an opinion.
Returning to behavioral performance on the X/K response inhibition task, this did not clearly differentiate between those inmates who would reoffend after release from those who did not. So we cannot conclude that cognitive factors are related to nonviolent criminal reoffending,5 at least from this one experiment that evaluated one specific executive function.
Footnotes
1 There were nine participants arrested for violent offenses, and of these six were arrested for both violent and nonviolent offenses. These latter subjects were an inaccurate bunch (42% commission errors), but it's hard to make much of such a small group.
2 The significance went down further if you controlled for age at release (for instance).
3 As reflected by the amplitude of the error-related negativity component, which is further modulated by motivational incentives and personality factors.
4 From my post on The Disconnection of Psychopaths:
5 WAIS scores were not predictive, either.
References
Aharoni, E., Vincent, G., Harenski, C., Calhoun, V., Sinnott-Armstrong, W., Gazzaniga, M., & Kiehl, K. (2013). Neuroprediction of future rearrest. Proceedings of the National Academy of Sciences. DOI: 10.1073/pnas.1219302110
Ermer E, Cope LM, Nyalakanti PK, Calhoun VD, Kiehl KA. (2012). Aberrant paralimbic gray matter in criminal psychopathy. J Abnorm Psychol. 121(3):649-58.
Gehring WJ, Goss B, Coles MGH, Meyer DE, Donchin E. (1993). A neural system for error-detection and compensation. Psychological Science 4:385–390.
Kiehl, K., Liddle, P., & Hopfinger, J. (2000). Error processing and the rostral anterior cingulate: An event-related fMRI study Psychophysiology, 37 (2), 216-223 DOI: 10.1111/1469-8986.3720216
Simons RF. (2010). The way of our errors: theme and variations. Psychophysiology 47(1):1-14.
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Neuroscience
Are Cognitive Factors Related to Criminal Reoffending?
Image from Graphic Sociology
Can Brain Activity Predict Criminal Reoffending? The previous post discussed a functional MRI study suggesting that the level of error-related activation in the anterior cingulate cortex (ACC) might have value in predicting whether a recently released prisoner will be rearrested within 4 years (Aharoni et al. 2013):
The odds that an offender with relatively low anterior cingulate activity would be rearrested were approximately double that of an offender with high activity in this region, holding constant other observed risk factors. These results suggest a potential neurocognitive biomarker for persistent antisocial behavior.
However, using ACC activity as a dichotomous variable misclassified 40% of low ACC participants who did not reoffend and 46% of high ACC participants who did commit crimes after release, not exactly the odds you'd want for making parole decisions. Even the senior author was doubtful that an fMRI test would ever be useful for risk assessment purposes on a case by case basis.
Since Aharoni and colleagues made their individual subject data available as supplementary material (Download Dataset_S01, XLSX), I was interested in how some of the demographic and performance variables might be related to recidivism, since these are obviously cheaper and easier to collect from incarcerated prisoners than MRI scans.
The cognitive task performed during the fMRI experiment required responding to a frequent stimulus presented 84% of the time ("X") and inhibiting responses to a rare stimulus ("K").
Fig. S4. (Aharoni et al., 2013). Go/No-Go task.
The study compared brain activity on incorrect responses to "K" (commission errors) and correct responses to "X" (hits) in a region of interest in the dorsal ACC, which has been implicated in error processing (Simons, 2010), among many other things. The authors framed the results largely in the context of impulse control, but other explanations are possible (as we'll see later).
Are any of the task performance variables related to recidivism? Starting with some very simple-minded t-tests, the rate of commission errors in the group of participants arrested for nonviolent offenses1 (n=40) did not differ significantly from what was seen in those not arrested again (n=56).2
Although there were data from a large control group of nonoffenders (n=102) used to set the ACC ROI, we don't have their behavioral results. I consulted an earlier fMRI paper by Kiehl et al. (2000) that used a very similar Go/NoGo task in 14 control participants. Commission errors occurred on 23.7% of NoGo Trials and omission errors on 3% of Go Trials, which is similar to what was seen in the offenders (overall means of 25.04% and 3.44%, respectively).
Reaction times (RTs) did not differ between the two offender groups either, suggesting there wasn't a differential speed-accuracy tradeoff (e.g., if the reoffenders were slower yet making marginally more errors).
Surprisingly, RTs were slower on commission errors (358 ms) than on hits (346 ms), a small but highly significant difference (p=.0005). This is the opposite of what you'd expect if the errors were due to impulsive responses. If the participants were becoming careless and not fully evaluating the NoGo stimulus, they'd be faster on error trials. This is why I'm not convinced the ACC activations are entirely related to behavioral impulsivity. In EEG studies of error processing, the degree of ACC activity3 is related to the emphasis placed on accuracy (Gehring et al., 1993), so if the reoffenders didn't care as much about accuracy, this could account for their low ACC status. One interesting bit of data for the authors to examine would be RT and accuracy on responses following an error, which indicates the amount of behavioral adjustment after making a mistake. Did the reoffenders show a lower propensity to slow down and become more careful? If so, this might reflect a lack of concern about the consequences of their actions.
However, the most puzzling thing to me were scores on Factor 2 of the Psychopathy Checklist-Revised (PCL-R) (Hare, 2003). Factor 2 is thought to reflect impulsivity, stimulation seeking, and irresponsibility (Ermer et al., 2012). The rearrested and not-rearrested groups were significantly different as expected, but in the opposite direction (unless I'm missing something here) — scores were lower in the group that was rearrested, in comparison to those who were not (p=.001).
In the paper, Aharoni and colleagues noted that age at release and Factor 2 scores showed predictive effects along with ACC activity. This was only when nonviolent crimes were considered [remember that only nine participants were arrested again for violent crimes]. Some research suggests that the PCL-R may predict violent recidivism, but other work questions this assertion.4 I'm definitely not the expert here, so please weigh in if you have an opinion.
Returning to behavioral performance on the X/K response inhibition task, this did not clearly differentiate between those inmates who would reoffend after release from those who did not. So we cannot conclude that cognitive factors are related to nonviolent criminal reoffending,5 at least from this one experiment that evaluated one specific executive function.
Footnotes
1 There were nine participants arrested for violent offenses, and of these six were arrested for both violent and nonviolent offenses. These latter subjects were an inaccurate bunch (42% commission errors), but it's hard to make much of such a small group.
2 The significance went down further if you controlled for age at release (for instance).
3 As reflected by the amplitude of the error-related negativity component, which is further modulated by motivational incentives and personality factors.
4 From my post on The Disconnection of Psychopaths:
Forensic psychologist Dr. Karen Franklin has written about multiple controversies surrounding the PCL-R, including the failure of Factor 1 to predict violence and Dr. Hare's attempt to block publication of a critical article. Also see this NPR series on Weighing The Value Of A Test For Psychopaths.
5 WAIS scores were not predictive, either.
References
Aharoni, E., Vincent, G., Harenski, C., Calhoun, V., Sinnott-Armstrong, W., Gazzaniga, M., & Kiehl, K. (2013). Neuroprediction of future rearrest. Proceedings of the National Academy of Sciences. DOI: 10.1073/pnas.1219302110
Ermer E, Cope LM, Nyalakanti PK, Calhoun VD, Kiehl KA. (2012). Aberrant paralimbic gray matter in criminal psychopathy. J Abnorm Psychol. 121(3):649-58.
Gehring WJ, Goss B, Coles MGH, Meyer DE, Donchin E. (1993). A neural system for error-detection and compensation. Psychological Science 4:385–390.
Kiehl, K., Liddle, P., & Hopfinger, J. (2000). Error processing and the rostral anterior cingulate: An event-related fMRI study Psychophysiology, 37 (2), 216-223 DOI: 10.1111/1469-8986.3720216
Simons RF. (2010). The way of our errors: theme and variations. Psychophysiology 47(1):1-14.
- Localising 'oops!' In The Brain
Researchers have identified the part of the brain that is activated when we make a costly mistake, and they think the same region may be implicated in conditions like obsessive compulsive disorder (OCD) that are associated with disproportionate anxiety...
- The Disconnection Of Psychopaths
Functional connectivity between the right amygdala and anterior vmPFC is reduced in psychopaths. From Fig. 2 of Motzkin et al., (2011). The last post discussed the case of a 14 yr old boy with congenital brain abnormalities and severe antisocial behavior...
- Studious Nerds Are Neurotic And Party Animals Are Antisocial
From the authors who brought you "Religion is the Xanax of the people" (aka Neural Markers of Religious Conviction) comes the finding that college students with lower grades are similar to religious zealots on a specific neural response to making an...
- Atheists Are Neurotic And Religious Zealots Are Antisocial
"Religion is the Xanax of the people" (Inzlicht et al., 2009). The clever quote above is from the latest paper to garner the _______ Are Neurotic and _______ Are Antisocial style of sensationalistic headline, a study that claims to reveal the Neural Markers...
- The Error Of Prognosticating Political View By Brain Wave
Figure 1. Liberals and conservatives in the study of Amodio et al. (2007) voted primarily for Kerry and Bush, respectively, in 2004. Brain wave recordings adapted from Mathalon et al. (2002). You've seen the headlines: Brains of liberals, conservatives...